From fcr-skills
Guides design of field experiments and crop models for Field Crops Research manuscripts: multi-environment trials, randomization, blocking, G×E analysis, and model calibration/validation.
How this skill is triggered — by the user, by Claude, or both
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/fcr-skills:fcr-experimental-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
FCR is demanding about **field-experimental rigour**. The design must credibly connect the agronomic
FCR is demanding about field-experimental rigour. The design must credibly connect the agronomic question to evidence that generalises across environments. The single most important FCR-specific rule: field experiments should, unless exceptional circumstances apply, span at least two seasons and/or multiple locations/environments. Design for that from the start.
For your design, write one sentence: "These environments represent ___, so the result is expected to hold for ___ (and not for ___)." If you cannot, the design does not yet support a general, FCR-worthy claim — add environments or scope the claim.
FCR referees expect the layout to follow from the agronomic question and the field's structure, with a named design and stated randomization. Pick — and justify — before committing plots.
| Situation | Design FCR expects | Note |
|---|---|---|
| One factor, field gradient | RCBD, blocks across the gradient | name blocks, give replication |
| Many genotypes, few reps | Resolvable incomplete block / alpha-lattice | recover inter-block information |
| Factor hierarchy (irrigation × N) | Split-plot (water = whole-plot) | report whole-plot + sub-plot error |
| Large/heterogeneous field | RCBD/lattice + spatial model (row–column, P-spline) | pre-plan the spatial term |
| Genotype ranking across environments | MET, environments a random sample | enables AMMI/GGE, stability inference |
No universal minimum exists, but FCR's ≥2-seasons/-environments expectation points to norms worth calibrating against — as illustrative anchors (confirm against your own variance): MET genotype trials often run ≥6–8 site-years before stability inference is credible; replication is commonly 3–4 blocks per environment; a response curve wants ≥4–5 levels.
Illustrative; the logic is the lesson. A team wants to claim a new wheat cultivar yields more under reduced N. A weak design — 1 site, 1 season, cultivar unreplicated — cannot separate cultivar from field position and yields no G×E information. The FCR-grade redesign: 2 seasons × 4 sites (8 environments) on a soil-N gradient, split-plot (N as whole-plot, cultivar as sub-plot), 4 blocks per environment, 5 N levels for a response curve, and a row–column spatial term — making the cultivar × N × environment surface identifiable and testable across environments.
fcr-topic-selection)【Design】RCBD / alpha-lattice / split-plot / MET / modelling
【Environments】#seasons × #sites; what they represent
【Randomization & replication】procedure + reps per environment
【G×E plan】fixed/random structure; stability analysis if relevant
【Environment characterisation】soil + weather vs. phenology recorded? [Y/N]
【Generalisation sentence】represents ___ → holds for ___
【Next】fcr-data-analysis
../../resources/external_tools.md — design packages (agricolae, FielDHub) and crop models (APSIM, DSSAT, STICS)../../resources/official-source-map.md — the ≥2-seasons/-environments rule and reproducibility expectationsnpx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin fcr-skillsGuides mixed-model analysis for Field Crops Research manuscripts: multi-environment trials, block/split-plot designs, G×E stability, and crop-model evaluation with proper error structure and means separation.
Guides design of manipulative experiments, observational studies, and process models for Global Change Biology manuscripts. Helps avoid pseudoreplication and match scale to claim.
Frames system boundaries, components, hierarchies, and feedbacks for an Agricultural Systems manuscript, then guides model selection, description, and calibration.